SIGMA leverages protein structural information to predict the pathogenicity of missense variants

作者：Hengqiang Zhao, Huakang Du, Sen Zhao, Zefu Chen, Yaqi Li, Kexin Xu, Bowen Liu, Xi Cheng, Wen Wen, Guozhuang Li, Guilin Chen, Zhengye Zhao, Guixing Qiu, Pengfei Liu, Jianguo Zhang, Zhihong Wu, Nan Wu · 发表于：Cell Reports Methods · 年份：2024 · DOI：10.1016/j.crmeth.2023.100687 · 被引用次数：26 · 研究领域：Genomics and Rare Diseases、Genomics and Phylogenetic Studies、RNA and protein synthesis mechanisms

Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.